Research on Cross-database Problem for Facial Expression Recognition and Age Classification
碩士 === 國立清華大學 === 資訊工程學系 === 100 === Most machine learning methods assumed training and test sets are independent and identically distributed, and may have degraded performance in practical applications since training and test sets are usually not independent and identically distributed. This is cal...
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| Format: | Others |
| Language: | en_US |
| Published: |
2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/24631991513003717158 |
| Summary: | 碩士 === 國立清華大學 === 資訊工程學系 === 100 === Most machine learning methods assumed training and test sets are independent and identically distributed, and may have degraded performance in practical applications since training and test sets are usually not independent and identically distributed. This is called cross-database problem. The ability of transfer learning has been identified to be helpful to solve the cross-database problem. In this thesis, we propose a novel transfer learning framework to utilize transfer learning and consider the discriminate information presented in the low-dimensional feature space. In experiment, we evaluate the effectiveness of our method on cross-database facial expression recognition and age classification. Our experiment shows that, our proposed framework outperforms conventional non-transferred subspace learning methods and most existing transfer subspace learning methods in both facial expression recognition and age classification.
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